Emotion recognition of speech signal using Taylor series and deep belief network based classification

Haridas, Arul Valiyavalappil; Marimuthu, Ramalatha; Sivakumar, V.; Chakraborty, Basabi

doi:10.1007/s12065-019-00333-3

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…Convolutional Neural Networks (CNNs) manifest in other studies [11][12][13], while [14]seamlessly blends RNNs with CNNs. A striking instance is [15] , wherein a Taylor series-based Deep Belief Network (Taylor-DBN) takes center stage. Similarly, [16] harnesses both a 1D CNN LSTM network and a 2D CNN LSTM network.…”

Section: Related Workmentioning

confidence: 99%

Speech Emotion Recognition in Multimodal Environments with Transformer: Arabic and English Audio Datasets

Mohamed,

Koura,

Kayed

2024

IJACSA

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Speech Emotion Recognition (SER) is a fastdeveloping area of study with a primary goal of automatically identifying and analyzing the emotional states expressed in speech. Emotions are crucial in human communication as they impact the effectiveness and meaning of linguistic expressions. SER aims to create computational approaches and models to detect and interpret emotions from speech signals. One of the primary applications of SER is evident in the field of Human-Computer Interaction (HCI), where it can be used to develop interactive systems that adapt to the user's emotional state based on their voice. This paper investigates the use of speech data for speech emotion recognition. Additionally, we applied a transformation process to convert the speech data into 2D images. Subsequently, we compared the outcomes of this transformation with the original speech data, aligning the comparison with a dataset containing labeled speech samples in both Arabic and English. Our experiments compare three methods: a transformer-based model, a Vision Transformer (ViT) based model, and a wave2vec-based model. The transformer model is trained from scratch on two significant audio datasets: the Arabic Natural Audio Dataset (ANAD) and the Toronto Emotional Speech Set (TESS), while the vision transformer is evaluated alongside wave2vec as part of transfer learning.The results are impressive. The transformer model achieved remarkable accuracies of 94% and 99% on ANAD and TESS datasets, respectively. Additionally, ViT demonstrates strong capabilities, achieving accuracies of 88% and 98% on the ANAD and TESS datasets, respectively. To assess the transfer learning potential, we also explore the Wave2Vector model with fine-tuning. However, the findings suggest limited success, achieving only a 56% accuracy rate on the ANAD dataset.

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Section: Related Workmentioning

confidence: 99%

Speech Emotion Recognition in Multimodal Environments with Transformer: Arabic and English Audio Datasets

Mohamed,

Koura,

Kayed

2024

IJACSA

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“…The discriminating powers of all biometric technologies rely on the extent of entropy, with the following used as performance indicators for biometric systems [ 584 , 585 , 586 , 587 ]: False match rate (FMR); False non-match rate (FNMR); Relative operating characteristic or receiver operating characteristic (ROC); Crossover error rate or equal error rate (CER or EER); Failure to enroll rate (FER or FTE), and Failure to capture rate (FTC).…”

Section: Evaluation Of Biometric Systemsmentioning

confidence: 99%

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Kaklauskas

Abraham

Ubarte

et al. 2022

Sensors

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Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik’s wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation’s success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.

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“…In essence, enabling humans to communicate with a computer is what drives ASR technology-forward [2]. ASR has many practical applications in speech emotion recognition (SER) in addition to medical diagnosis and call centres, diverse systems such as safe car driving, automatic translation, mobile telecommunication greatly benefit from speech emotion recognition (SER) as a manifestation of ASR [3]. However, these studies significantly suffer from time asymmetry, instability, a low signal-to-noise ratio and uncertain brain areas of specific reactions, resulting in unreliable results [4].…”

Section: Introductionmentioning

confidence: 99%

“…Also, the practicality of developed algorithms to minimize shortcomings has been hindered by unexpected issues [6]. model proposed by Haidas et al have effectively addressed these issues [3]. Supported by the Taylor series, the Taylor-Deep Belief Network model updates the weights and the bias for the Deep Belief Network (DBN) training.…”

Section: Introductionmentioning

confidence: 99%

Speech Emotion Recognition Using ANFIS and PSO-optimization With Word2Vec

2023

JNS

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Speech Emotion Recognition (SER) plays a vital role in human-computer interaction as an important branch of affective computing. Due to inconsistencies in the data and challenging signal extraction, in this paper, we propose a novel emotion recognition method based on the combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) with Word to Vector (Word2Vec) models. To begin, the inputs have been pre-processed, which comprise audio and text data. Second, the features were extracted using the Word2vec behind spectral and prosodic approaches. Finally, the features are selected using the Sequential Backward Floating Selection (SBFS) approach. In the end, the ANFIS-PSO model has been used to recognize speech emotion. A performance evaluation of the proposed algorithm is carried out on Sharif Emotional Speech Database (ShEMO). The experimental results show that the proposed algorithm has advantages in accuracy, reaching 0.873 and 0.752 in males and females, respectively, in comparison with the CNNs and SVM, MLP, RF models.

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Emotion recognition of speech signal using Taylor series and deep belief network based classification

Cited by 11 publications

References 22 publications

Speech Emotion Recognition in Multimodal Environments with Transformer: Arabic and English Audio Datasets

Speech Emotion Recognition in Multimodal Environments with Transformer: Arabic and English Audio Datasets

A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States

Speech Emotion Recognition Using ANFIS and PSO-optimization With Word2Vec

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